The proposed study will be a multi-level survey of environmental risk factors thought to contribute to obesity prevalence. In the proposed study, we will collect both individual-level health data including weight and body mass index (BMI), status on other health conditions and behaviors, as well information on the density of social institutions (e.g., fast-food restaurants) and their actual locations, and the conditions of the homes, grounds, and public infractrastructure within selected census block-groups and their surrounding areas (a 0.75 mile radius around the perimeter of each block-group). The primary aims and hypotheses are as follows: Aim 1: To examine differences in obesity prevalence among census block-groups stratified by household income level (our pilot data suggest that household income level is a powerful proxy variable for the presence of obesity-producing environmental factors). We hypothesize that obesity prevalence will be significantly, and monotonically related to block-group income, with lower income groups having higher obesity prevalence; Aim 2: To examine the density of potential obesity-promoting or protective environmental factors among census block-groups stratified by household income level. These factors include a greater density of fast-food restaurants, convenience stores, alcohol-selling outlets (e.g., bars, liquor stores) and fewer full-service supermarkets, parks, and recreational facilities. We hypothesize that obesity-promoting factors will occur with greater frequency and obesity-protective factors with lesser frequency in lower income areas; Aim 3: To examine differences in whether individual level diet and physical activity are predicted by the density of obesity promoting or protective factors. We hypothesize that high caloric intake will be predicted by greater density of obesity-promoting institutions (e.g., fast-food restaurants, convenience stores) and few obesity-protective factors (e.g., full-service grocery stores). In contrast, lower caloric expenditure will be predicted by lower density of obesity protective factors (e.g., recreational and exercise facilities). The resulting dataset would represent the first measured (rather than estimated via secondary sources), comprehensive (rather than focusing on one or two environmental factors), and detailed (rather than being limited to existing archival data) profile of a variety of urban environments linked to both individual health behaviors and obesity prevalence. In addition, our analyses will add strong credence to the causal inference that environmental characteristics promote obesity through their influence on individual health behaviors. It is critical to note that such basic data are lacking in the literature.